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Usefulness of risk stratification models for colorectal cancer based on fecal hemoglobin concentration and clinical risk factors.
Gastrointestinal Endoscopy 2019 June
BACKGROUND AND AIMS: We aimed to develop risk stratification models for advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) based on fecal hemoglobin (f-Hb) concentration and clinical risk factors.
METHODS: We reviewed screenees aged ≥50 years who underwent fecal immunochemical test (FIT) and colonoscopy and developed risk-scoring models for ACRN and CRC using logistic regression analysis. Participants were classified into low- (risk lower than that in FIT-negative individuals), intermediate- (risk higher than that in FIT-negative but lower than that in FIT-positive individuals), high- (risk similar to that in FIT-positive individuals), and very-high- (risk higher than that in FIT-positive individuals) risk groups.
RESULTS: Of 3733 participants, 367 (9.8%) and 70 (1.9%) had ACRN and CRC, respectively. On multivariable analysis, age (ß = .043/y), former smoker (ß = .401), current smoker (ß = .841), diabetes (ß = .097), and square root of f-Hb concentration (ß = .071) were significantly associated with ACRN. In terms of CRC, age (ß = .035/y) and square root of f-Hb concentration (ß = .004) were associated factors. After point assignments based on the regression coefficient, we could classify screenees as low-, intermediate-, high-, and very-high-risk groups. ACRN was identified in 2.9%, 5.3%, 16.2%, and 35.7% of screenees in the low-, intermediate-, high-, and very-high-risk groups, respectively. CRC was identified in .1%, .5%, 3.9%, and 11.1% of screenees in the low-, intermediate-, high-, and very-high-risk groups, respectively.
CONCLUSIONS: The proposed models can effectively stratify the risk for ACRN and CRC and provide accurate information on this risk in individuals who undergo FIT.
METHODS: We reviewed screenees aged ≥50 years who underwent fecal immunochemical test (FIT) and colonoscopy and developed risk-scoring models for ACRN and CRC using logistic regression analysis. Participants were classified into low- (risk lower than that in FIT-negative individuals), intermediate- (risk higher than that in FIT-negative but lower than that in FIT-positive individuals), high- (risk similar to that in FIT-positive individuals), and very-high- (risk higher than that in FIT-positive individuals) risk groups.
RESULTS: Of 3733 participants, 367 (9.8%) and 70 (1.9%) had ACRN and CRC, respectively. On multivariable analysis, age (ß = .043/y), former smoker (ß = .401), current smoker (ß = .841), diabetes (ß = .097), and square root of f-Hb concentration (ß = .071) were significantly associated with ACRN. In terms of CRC, age (ß = .035/y) and square root of f-Hb concentration (ß = .004) were associated factors. After point assignments based on the regression coefficient, we could classify screenees as low-, intermediate-, high-, and very-high-risk groups. ACRN was identified in 2.9%, 5.3%, 16.2%, and 35.7% of screenees in the low-, intermediate-, high-, and very-high-risk groups, respectively. CRC was identified in .1%, .5%, 3.9%, and 11.1% of screenees in the low-, intermediate-, high-, and very-high-risk groups, respectively.
CONCLUSIONS: The proposed models can effectively stratify the risk for ACRN and CRC and provide accurate information on this risk in individuals who undergo FIT.
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